Daniel Hein

34 papers A 1B 3Misc 2Journal 22Unranked 5
YearRankTypeTitle / Venue / Authors
2025 J jnl
From Classical Data to Quantum Advantage - Quantum Policy Evaluation on Quantum Hardware.
CoRR
Daniel Hein, Simon Wiedemann, Markus Baumann, Patrik Felbinger, Justin Klein, Maximilian Schieder, Jonas Stein, Daniëlle Schuman, Thomas Cope, Steffen Udluft
2025 J jnl
Is Q-learning an Ill-posed Problem?
CoRR
Philipp Wissmann, Daniel Hein, Steffen Udluft, Thomas A. Runkler
2025 J jnl
Variational Quantum Circuits in Offline Contextual Bandit Problems.
CoRR
Lukas Schulte, Daniel Hein, Steffen Udluft, Thomas A. Runkler
2024 J jnl
First steps towards real-world traffic signal control optimisation by reinforcement learning.
J. Simulation
Henri Meess, Jeremias Gerner, Daniel Hein, Stefanie Schmidtner, Gordon Elger, Klaus Bogenberger
2024 Misc conf
Model-Based Offline Quantum Reinforcement Learning.
QCE
Simon Eisenmann, Daniel Hein, Steffen Udluft, Thomas A. Runkler
2024 J jnl
Model-based Offline Quantum Reinforcement Learning.
CoRR
Simon Eisenmann, Daniel Hein, Steffen Udluft, Thomas A. Runkler
2024 B conf
Why long model-based rollouts are no reason for bad Q-value estimates.
ESANN
Philipp Wissmann, Daniel Hein, Steffen Udluft, Volker Tresp
2024 J jnl
Why long model-based rollouts are no reason for bad Q-value estimates.
CoRR
Philipp Wissmann, Daniel Hein, Steffen Udluft, Volker Tresp
2023 — conf
Learning Control Policies for Variable Objectives from Offline Data.
SSCI
Marc Weber, Phillip Swazinna, Daniel Hein, Steffen Udluft, Volkmar Sterzing
2023 J jnl
Learning Control Policies for Variable Objectives from Offline Data.
CoRR
Marc Weber, Phillip Swazinna, Daniel Hein, Steffen Udluft, Volkmar Sterzing
2023 J jnl
Quantum Policy Iteration via Amplitude Estimation and Grover Search - Towards Quantum Advantage for Reinforcement Learning.
Trans. Mach. Learn. Res.
Simon Wiedemann, Daniel Hein, Steffen Udluft, Christian B. Mendl
2023 Misc conf
Workshop Summary: Quantum Machine Learning.
QCE
Volker Tresp, Steffen Udluft, Daniel Hein, Werner Hauptmann, Martin Leib, Christopher Mutschler, Daniel D. Scherer, Wolfgang Mauerer
2022 J jnl
Comparing Model-free and Model-based Algorithms for Offline Reinforcement Learning.
CoRR
Phillip Swazinna, Steffen Udluft, Daniel Hein, Thomas A. Runkler
2022 J jnl
Quantum Policy Iteration via Amplitude Estimation and Grover Search - Towards Quantum Advantage for Reinforcement Learning.
CoRR
Simon Wiedemann, Daniel Hein, Steffen Udluft, Christian B. Mendl
2022 A conf
Reinforcement Learning for Traffic Signal Control Optimization: A Concept for Real-World Implementation.
AAMAS
Henri Meess, Jeremias Gerner, Daniel Hein, Stefanie Schmidtner, Gordon Elger
2021 B conf
Behavior Constraining in Weight Space for Offline Reinforcement Learning.
ESANN
Phillip Swazinna, Steffen Udluft, Daniel Hein, Thomas A. Runkler
2021 J jnl
Behavior Constraining in Weight Space for Offline Reinforcement Learning.
CoRR
Phillip Swazinna, Steffen Udluft, Daniel Hein, Thomas A. Runkler
2021 J jnl
Trustworthy AI for Process Automation on a Chylla-Haase Polymerization Reactor.
CoRR
Daniel Hein, Daniel Labisch
2021 — conf
Trustworthy AI for process automation on a Chylla-Haase polymerization reactor.
GECCO Companion
Daniel Hein, Daniel Labisch
2020 J jnl
Interpretable Control by Reinforcement Learning.
CoRR
Daniel Hein, Steffen Limmer, Thomas A. Runkler
2019 — conf
Generating interpretable reinforcement learning policies using genetic programming.
GECCO (Companion)
Daniel Hein, Steffen Udluft, Thomas A. Runkler
2019 — —
Interpretable Reinforcement Learning Policies by Evolutionary Computation.
Daniel Hein
2018 J jnl
Generating Interpretable Fuzzy Controllers using Particle Swarm Optimization and Genetic Programming.
CoRR
Daniel Hein, Steffen Udluft, Thomas A. Runkler
2018 — conf
Generating interpretable fuzzy controllers using particle swarm optimization and genetic programming.
GECCO (Companion)
Daniel Hein, Steffen Udluft, Thomas A. Runkler
2018 J jnl
Interpretable policies for reinforcement learning by genetic programming.
Eng. Appl. Artif. Intell.
Daniel Hein, Steffen Udluft, Thomas A. Runkler
2017 J jnl
A Benchmark Environment Motivated by Industrial Control Problems.
CoRR
Daniel Hein, Stefan Depeweg, Michel Tokic, Steffen Udluft, Alexander Hentschel, Thomas A. Runkler, Volkmar Sterzing
2017 — conf
A benchmark environment motivated by industrial control problems.
SSCI
Daniel Hein, Stefan Depeweg, Michel Tokic, Steffen Udluft, Alexander Hentschel, Thomas A. Runkler, Volkmar Sterzing
2017 J jnl
Batch Reinforcement Learning on the Industrial Benchmark: First Experiences.
CoRR
Daniel Hein, Steffen Udluft, Michel Tokic, Alexander Hentschel, Thomas A. Runkler, Volkmar Sterzing
2017 B conf
Batch reinforcement learning on the industrial benchmark: First experiences.
IJCNN
Daniel Hein, Steffen Udluft, Michel Tokic, Alexander Hentschel, Thomas A. Runkler, Volkmar Sterzing
2017 J jnl
Interpretable Policies for Reinforcement Learning by Genetic Programming.
CoRR
Daniel Hein, Steffen Udluft, Thomas A. Runkler
2017 J jnl
Particle swarm optimization for generating interpretable fuzzy reinforcement learning policies.
Eng. Appl. Artif. Intell.
Daniel Hein, Alexander Hentschel, Thomas A. Runkler, Steffen Udluft
2016 J jnl
Introduction to the "Industrial Benchmark".
CoRR
Daniel Hein, Alexander Hentschel, Volkmar Sterzing, Michel Tokic, Steffen Udluft
2016 J jnl
Particle Swarm Optimization for Generating Fuzzy Reinforcement Learning Policies.
CoRR
Daniel Hein, Alexander Hentschel, Thomas A. Runkler, Steffen Udluft
2016 J jnl
Reinforcement Learning with Particle Swarm Optimization Policy (PSO-P) in Continuous State and Action Spaces.
Int. J. Swarm Intell. Res.
Daniel Hein, Alexander Hentschel, Thomas A. Runkler, Steffen Udluft